A neural network can generate high-resolution 3D terrain models far faster than traditional methods. The technique has immediate applications in gaming and film, where creating realistic landscapes usually takes weeks of manual work.
Dear fellow scholars, this is two minute papers with zsolnai.
We have recently witnessed the emergence of.
Neural network based techniques that are able.
To synthesize all sorts of images. Our previous episode was about Nvidia's algorithm that created high resolution images of imaginary celebrities.
That was a really cool application of generative adversarial networks.
This architecture means that we have a.
Pair of neural networks.
One that learns to generate new images and the other learns to tell a fake image from a real one. And as they compete against each other, they get better and better without any human interaction. So we can clearly use them to create 2D images. But why stop there? Why not use this technique, for instance, to create assets for digital media? So instead of 2D images, let's try to adapt these networks to Generate high resolution 3D models of terrains that we can use to populate a virtual world. Both computer games and the motion picture industry could benefit greatly from such a tool. This process is typically done via procedural generation, which is basically a sort of guided random terrain generation. Here we can have a more direct effect on the output without putting in.
Tens of hours of work to get the job done.
In this first training step, this technique learns how an image of a terrain.
Corresponds to input drawings.
Then we will be able to sketch a draft of a landscape with rivers, ridges, valleys, and the algorithm will output a high quality model of the terrain itself. During this process, we can have a look at the current output and refine our drawings in the meantime, leading to.
A super efficient process where we can.
Go from a thought to high quality final results within a few seconds without being bogged down with the technical details.
What's more, it can also not only.
Deal with erased sub regions, but it can also automatically fill them with sensible information to save time for us. What an outstanding convenience feature. And the algorithm can also perform physical manipulations like erosion to the final results. After the training for the erosion step is done, the computational cost is practically zero. For instance, running an erosion simulator on this piece of data would take around.
40 seconds, where the neural network can.
Do it in 25 milliseconds. The full simulation would almost be a minute, where the network can mimic its results practically instantaneously.
A limitation of this technique is that.
If the input is too sparse, unpleasant.
Grid artifacts may appear.
There are tons of more cool features in the paper. Make sure to have a look. As always, it is available in the video description.
This is a really well thought out.
And well presented work that I expect to be a true powerhouse for terrain authoring.
In the future.
And in the meantime, we have reached.
100,000 subscribers, 100,000 fellow scholars.
Wow. This is absolutely amazing.
And honestly, I never thought that this would ever happen.
So happy paperversary. Thank you very much for coming along.
On this journey of science, and I.
Am very happy to see that the.
Series brings joy and learning to more people than ever.
Thanks for watching and for your generous.
Support, and I'll see you next time.
Sam.
